POSTED : octubre 11, 2016
BY : Ryan Knauber

You’ve probably heard or used the term “analytics” in the past, most likely when talking about some level of deep information analysis. The problem is it’s fairly nondescript. If you’re at a fancy cocktail party and ask someone what they do for a living, and they respond “I do analytics,” what are you imagining? Do you think they look at numbers all day? Do you think they’re certified in that Big Data thing you read about once? Do you think they just cleverly deflected your question? The term has slowly died over time in favor of “Data Science“, and for good reason. What people slowly began to realize was that working with data requires a scientific view. Its purpose is not to go out and confirm the existing ideas/opinions of management or executives. The purpose is for the analyst to observe the data, report the answers it provides, and potentially model the current state to predict the future.

It’s science because its central concern is finding the truth. What is our true rate of customer turnover? What is our true market share? What is our true return on R&D? Finding this truth takes extensive work and expertise, and one of the best descriptions of what Data Science encompasses is Drew Conway’s Data Science Venn Diagram:


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